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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# TODO\n",
      "- how to post data? small data file?\n",
      "\n",
      "# Predicting NBA game wins\n",
      "\n",
      "In this notebook, we'll form a simple model to predict NBA game winners based on which teams are playing, whether they are playing at home or away, and which players are playing in the game and for how long.\n",
      "\n",
      "The goal is to demonstrate implementing a simple (but non-standard) regression model with CVXPY.\n",
      "\n",
      "# Imports and helper functions\n",
      "\n",
      "In the cell below, we define some helper functions to load the data, manipulate it, format it, and plot the results.\n",
      "\n",
      "The actual optimization code will be given in-line later in the notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os  \n",
      "import json\n",
      "import datetime\n",
      "from collections import defaultdict\n",
      "import numpy as np\n",
      "import cvxpy as cvx\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib\n",
      "matplotlib.rcParams.update({'font.size': 14})\n",
      "\n",
      "class PlayerAppearance(object):\n",
      "    __slots__= \"name\", \"id\", \"minutes\", 'team'\n",
      "    \n",
      "class TeamAppearance(object):\n",
      "    __slots__ = 'id', 'score', 'players', 'home', 'gameid'\n",
      "\n",
      "class Game(object):\n",
      "    __slots__ = 'winner', 'loser', 'id', 'season'\n",
      "    \n",
      "\n",
      "def get_game_data():\n",
      "    '''Return a list of game objects from `gamestats` directory.'''\n",
      "    games = []\n",
      "\n",
      "    for fn in os.listdir('gamestats'):\n",
      "         if fn.endswith('.json'):\n",
      "            with open('gamestats/'+fn) as f:\n",
      "                a = json.load(f)\n",
      "\n",
      "                hometeamid = int(a['resultSets'][0]['rowSet'][0][6])\n",
      "                game = Game()\n",
      "                game.season = int(a['resultSets'][0]['rowSet'][0][8])\n",
      "                game.id = int(a['resultSets'][0]['rowSet'][0][2])\n",
      "\n",
      "                teams = [TeamAppearance(), TeamAppearance()]\n",
      "                for i, jteam in enumerate(a['resultSets'][1]['rowSet']):\n",
      "                    teams[i].id = str(jteam[4])\n",
      "                    teams[i].score = jteam[21]\n",
      "                    teams[i].home = jteam[3] == hometeamid\n",
      "                    teams[i].gameid = game.id\n",
      "                    teams[i].players = []\n",
      "\n",
      "                for jplayer in a['resultSets'][4]['rowSet']:\n",
      "                    if jplayer[8] == None or jplayer[8] == 0.0:\n",
      "                        continue\n",
      "                    player = PlayerAppearance()\n",
      "                    player.name = str(jplayer[5])\n",
      "                    player.id = jplayer[4]\n",
      "                    minutes = jplayer[8].split(':')\n",
      "                    minutes = float(minutes[0]) + float(minutes[1])/60.0\n",
      "                    player.minutes = minutes\n",
      "                    player.team = str(jplayer[2])\n",
      "\n",
      "                    for team in teams:\n",
      "                        if player.team == team.id:\n",
      "                            team.players.append(player)\n",
      "\n",
      "                if teams[0].score > teams[1].score:\n",
      "                    game.winner, game.loser = teams[0], teams[1]\n",
      "                else:\n",
      "                    game.winner, game.loser = teams[1], teams[0]\n",
      "\n",
      "                games.append(game)\n",
      "    return games\n",
      "\n",
      "def index_mappings(games):\n",
      "    '''Return mappings between player and game identifiers and an indexed ordering.\n",
      "    \n",
      "    Puts teams and players appearing in `games` in an arbitrary order.\n",
      "    This is the ordering which will be used to determine the relationship between\n",
      "    matrix rows in the matrix-stuffing step.\n",
      "    \n",
      "    Returns:\n",
      "    teams    --  ordered list of teams\n",
      "                 (index -> team)\n",
      "    players  --  ordered list of player pairs: (player name, unique player ID)\n",
      "                 (index -> player)\n",
      "    team2idx --  mapping team to index\n",
      "                 (team -> index)\n",
      "    pid2idx  --  mapping from unique player id to index\n",
      "                 (player -> index)\n",
      "    '''\n",
      "    teams = set()\n",
      "    players = set()\n",
      "    for game in games:\n",
      "        for team in game.winner, game.loser:\n",
      "            teams.add(team.id)\n",
      "            for player in team.players:\n",
      "                players.add((player.name,player.id))\n",
      "                \n",
      "\n",
      "    teams = list(teams)\n",
      "    players = list(players)\n",
      "    \n",
      "    team2idx = {t: i for i, t in enumerate(teams)}\n",
      "    pid2idx = {t[1]: i for i, t in enumerate(players)}\n",
      "    \n",
      "    return teams, players, team2idx, pid2idx\n",
      "\n",
      "def split_games(games,p=0.3):\n",
      "    '''Return games split into disjoint training and hold-out sets.'''\n",
      "    \n",
      "    games_holdout = np.random.choice(len(games),size=int(len(games)*p),replace=False)\n",
      "    \n",
      "    # training set\n",
      "    games_t = [games[i] for i in range(len(games)) if i not in games_holdout]\n",
      "    \n",
      "    # hold-out set\n",
      "    games_h = [games[i] for i in games_holdout]\n",
      "    \n",
      "    return games_t, games_h, \n",
      "\n",
      "def data_mats(games, team2idx,home=False,pid2idx=[]):\n",
      "    '''Stuff feature matrices for fitting model.\n",
      "    \n",
      "    Features included in the matrices depend on the function inputs.\n",
      "    \n",
      "    data_mats(games, team2idx) -- Team identities are the only features.\n",
      "        That is, W_ij = 1 if team j won game i. 0 otherwise.\n",
      "        \n",
      "    data_mats(games, team2idx, home=True) -- Appends a column to W and L corresponding to\n",
      "        whether the team was the home team.\n",
      "        \n",
      "    data_mats(games, team2idx,home=True,pid2idx=pid2idx) -- Appends columns\n",
      "        to W and L corresponding to which players played and what fraction\n",
      "        of 48 minutes they played in the game. That is, *if* j corresponds\n",
      "        to a player column, then W_ij = t/48, where t is how many minutes\n",
      "        player j (from the winning team) played in game i.\n",
      "    '''\n",
      "    m, n = len(games), len(team2idx)\n",
      "    W, L = np.zeros((m,n)), np.zeros((m,n))\n",
      "    \n",
      "    Wh, Lh = np.zeros((m,1)), np.zeros((m,1))\n",
      "    \n",
      "    n = len(pid2idx)\n",
      "    Wp, Lp = np.zeros((m,n)), np.zeros((m,n))\n",
      "\n",
      "    for i, game in enumerate(games):\n",
      "        for A, Ah, Ap, team in [(W, Wh, Wp, game.winner),(L, Lh, Lp, game.loser)]:\n",
      "            A[i,team2idx[team.id]] = 1\n",
      "            if team.home:\n",
      "                Ah[i] = 1\n",
      "            if len(pid2idx) > 0:\n",
      "                for player in team.players:\n",
      "                    Ap[i,pid2idx[player.id]] = player.minutes/48\n",
      "                    \n",
      "    if home:\n",
      "        W = np.hstack([W, Wh])\n",
      "        L = np.hstack([L, Lh])\n",
      "    if len(pid2idx) > 0:\n",
      "        W = np.hstack([W, Wp])\n",
      "        L = np.hstack([L, Lp])\n",
      "    \n",
      "    return W, L\n",
      "\n",
      "def percent_correct(W,L,w):\n",
      "    '''Return percentage correctly predicted for games (W,L) and model parameters w.\n",
      "    \n",
      "    W and L can be from training or hold-out data.\n",
      "    '''\n",
      "    m, n = W.shape\n",
      "    return float(sum((W-L).dot(w) > 0))/m\n",
      "\n",
      "def reg_path(games,holdout,gammas,N, home=False, playertime=False):\n",
      "    '''Plot a regularization path.\n",
      "    \n",
      "    For each of N times, partition games into a training\n",
      "    and hold-out set, with `holdout` giving the hold-out proportion.\n",
      "    For each gamma in gammas, train the model on the training set\n",
      "    and evaluate it on both the training and hold out sets.\n",
      "    \n",
      "    Plot a data point for each instance with gamma on the x-axis\n",
      "    and the percent correct on the y-axis. Also plot a line showing\n",
      "    the average for each gamma.\n",
      "    \n",
      "    Optional arguments:\n",
      "    home -- include home team feature\n",
      "    playertime -- include player minutes played features\n",
      "    '''\n",
      "    v_t = []\n",
      "    v_h = []\n",
      "    \n",
      "    teams, players, team2idx, pid2idx = index_mappings(games)\n",
      "    if playertime is False:\n",
      "        pid2idx = []\n",
      "\n",
      "    for i in range(N):\n",
      "        print '\\nPartition {}'.format(i),\n",
      "        games_t, games_h = split_games(games, holdout)\n",
      "        W, L = data_mats(games_t, team2idx, home, pid2idx)\n",
      "        Wh, Lh = data_mats(games_h, team2idx, home, pid2idx)\n",
      "        for gamma in gammas:\n",
      "            print '.',\n",
      "            x = fit(W,L,gamma)\n",
      "            v_t.append((gamma,percent_correct(W,L,x)))\n",
      "            v_h.append((gamma,percent_correct(Wh,Lh,x)))\n",
      "            \n",
      "    alpha = .3\n",
      "    fig, ax = plt.subplots(2, 1, sharex=True)\n",
      "    fig.set_size_inches((10,10))\n",
      "    a,b = zip(*v_t)\n",
      "    ax[0].scatter(a,b,alpha=alpha)\n",
      "    ax[0].set_ylabel('Training set % correct')\n",
      "    a,b = zip(*v_h)\n",
      "    ax[1].scatter(a,b,alpha=alpha)\n",
      "    ax[1].set_ylabel('Test set % correct')\n",
      "    ax[1].set_xlabel('Regularization $\\gamma$')\n",
      "\n",
      "    for i, path in enumerate([v_t,v_h]):\n",
      "        avg = defaultdict(float)\n",
      "        tot = defaultdict(float)\n",
      "        for gamma, pred in path:\n",
      "            avg[gamma] += pred\n",
      "            tot[gamma] += 1.0\n",
      "\n",
      "        for gamma in avg.keys():\n",
      "            avg[gamma] /= tot[gamma]\n",
      "\n",
      "        gammas = sorted(avg.keys())\n",
      "        vals = [avg[key] for key in gammas]\n",
      "        ax[i].plot(gammas, vals, 'r' )\n",
      "\n",
      "    ax[0].legend(('average','data partition instance'))\n",
      "    \n",
      "    return v_t, v_h"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Raw data\n",
      "\n",
      "Below, we load the NBA game data from the 'gamestats' folder. We have games from NBA seasons 2010-2013. For each game, we identify the winning and losing team and the NBA season in which the game was played. For each team appearance in a game, we have the score, a list of player appearances, and whether the team played at home or away. For each player appearance in a game, we have the player's name, a unique player ID, and the number of minutes they played in the game.\n",
      "\n",
      "# Model\n",
      "\n",
      "We will train our model on a set of $m$ games.\n",
      "The data point for game $i$ consists of an ordered pair of feature vectors, $(x^w_i, x^l_i) \\in \\mathbf{R}^n \\times \\mathbf{R}^n$.\n",
      "The first vector in the pair represents the winning team. Feature vectors consist of $n$ features which may include which team is playing, if they are the home team, and how many minutes each player on the team played.\n",
      "\n",
      "We want to find model parameters $w$ for the classifier function $f_w(x,y) = (x-y)^T w$.\n",
      "The model predicts that a team with features $x$ will beat a team with features $y$ if $f_w(x,y) > 0$.\n",
      "Note that the classification inequality is homogenous in $w$.\n",
      "That is, the classifier is invariant to scalings of $w$.\n",
      "\n",
      "To properly predict all games, we would need $f_w(x^w_i, x^l_i) > 0$ for all games $i$.\n",
      "This set of homogenous strict inequalities in $w$ is feasible if and only if\n",
      "there is some scaling $\\tilde{w} = \\alpha w$ such that\n",
      "$f_{\\tilde{w}}(x^w_i, x^l_i) \\geq 1$ for all $i$. This motivates the loss function which we'll use:\n",
      "\n",
      "\\begin{equation}\n",
      "L(w) = \\frac{1}{m}\\sum_{i=1}^m \\max \\lbrace 1 - (x^w_i - x^l_i)^T w, 0 \\rbrace.\n",
      "\\end{equation}\n",
      "\n",
      "The function $L(w)$ assigns a positive loss whenever the classification inequality $f_w(x^w_i, x^l_i) \\geq 1$\n",
      "is violated.\n",
      "\n",
      "We'll choose $w$ by minimizing the loss $L(w)$, plus a regularization term $\\gamma \\| w \\|_2$.\n",
      "The regularization term is used to prevent over-fitting.\n",
      "\n",
      "We use the `fit(W,L,gamma)` function defined below to solve the optimization problem\n",
      "\n",
      "\\begin{array}{ll}\n",
      "\\mbox{minimize} & L(w) + \\gamma \\| w \\|_2,\n",
      "\\end{array}\n",
      "\n",
      "where $W,L \\in \\mathbf{R}^{m \\times n}$ are data matrices, with row $i$ of $W$ ($L$) corresponding to $x^w_i$ ($x^l_i$)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fit(W,L,gamma):\n",
      "    '''Return model parameters w trained on data (W,L) with regularization gamma.'''\n",
      "    m,n = W.shape\n",
      "    w = cvx.Variable(shape=(n,1))\n",
      "\n",
      "    objective = cvx.sum(cvx.pos(1 - (W-L)*w))/m + gamma*cvx.norm(w)\n",
      "    objective = cvx.Minimize( objective )\n",
      "    prob = cvx.Problem(objective)\n",
      "    result = prob.solve(solver=cvx.ECOS, verbose=False)\n",
      "    if prob.status != 'optimal':\n",
      "        print \"ERROR!\"\n",
      "    return np.array(w.value).flatten()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Example\n",
      "\n",
      "Load the list of games from the `gamestats` directory and select only the games from a single season."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "games = get_game_data()\n",
      "\n",
      "# select just games in a single season\n",
      "games = [game for game in games if game.season == 2010]\n",
      "len(games)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "1230"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Choose some ordering of the teams and players for indexing purposes, and get a mapping between the index and unique identifiers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "teams, players, team2idx, pid2idx = index_mappings(games)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Randomly split the games so that 70% are in a training set and 30% are in a hold-out set. Produce the data matrices $W$ and $L$ for the training and hold-out sets. Use features giving the team id, if they are the home team, and the time played for each player."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(2)\n",
      "games_t, games_h = split_games(games, .3)\n",
      "\n",
      "W, L = data_mats(games_t, team2idx, True, pid2idx)\n",
      "Wh, Lh = data_mats(games_h, team2idx, True, pid2idx)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Train the model with **no** regularization on the training set and evaluate the classification performance on both the training and hold-out set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gamma = 0\n",
      "w = fit(W,L,gamma)\n",
      "\n",
      "print \"Training set %% correct: %f\"%percent_correct(W,L,w)\n",
      "print \"Hold-out set %% correct: %f\"%percent_correct(Wh,Lh,w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training set % correct: 1.000000\n",
        "Hold-out set % correct: 0.582656\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that we've over fit the model because the classification is perfect on the training set, but low on the hold-out set. Adding some regularization should improve the predictive performance on the hold-out set:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gamma = .03\n",
      "w = fit(W,L,gamma)\n",
      "\n",
      "print \"Training set %% correct: %f\"%percent_correct(W,L,w)\n",
      "print \"Hold-out set %% correct: %f\"%percent_correct(Wh,Lh,w)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training set % correct: 0.786295\n",
        "Hold-out set % correct: 0.682927\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Regularization paths\n",
      "\n",
      "We'll find good choices for the regularization hyper-parameter $\\gamma$ by plotting a regularization path.\n",
      "We'll randomly partition the data into training and hold-out sets and plot the percentage of games predicted correctly over a range of choices of $\\gamma$. We'll do this several times for each choice of $\\gamma$ and plot the average result.\n",
      "\n",
      "We'll plot the regularization path over different sets of features. First, we'll just use team identities, then we'll add whether the team played at home, and then we'll add the times played of each player.\n",
      "\n",
      "We should see an increase in predictive power as we add features.\n",
      "\n",
      "Note that some of these examples may take a few minutes to run.\n",
      "\n",
      "## Team identities\n",
      "\n",
      "When the features include just the team identities, the model reduces to just assigning a ranking to each team. This model is so simple that it is hard to over-fit it on our data, so regularization has little effect."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(0)\n",
      "gammas = np.linspace(0,1,20)\n",
      "v_t, v_h = reg_path(games,.3,gammas,5,False, False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Partition 0 . . "
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        ". . "
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      },
      {
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       "text": [
        "\n",
        "Partition 1 . . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      },
      {
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      {
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Partition 2 . . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
       ]
      },
      {
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3h4SEjhbHCfDbb7vZt+86Wq0f7u5/MnhwGyIizBlfVL6LFy+yf38u0dHd0Wg0ZGVl8NNP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\n1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       "text": [
        "<matplotlib.figure.Figure at 0x11465c2d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Identities + home/away\n",
      "\n",
      "We add a single feature denoting whether the teams played at home. This doesn't add much to our model complexity, so it is still difficult to over-fit. The predictive power is about the same as the previous model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(0)\n",
      "gammas = np.linspace(0,.1,50)\n",
      "v_t, v_h = reg_path(games,.3,gammas,5,True, False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Partition 0 . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". . "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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F6pp0JJ8Gs1qJ3x+D/+Y0yM4GoHDvITZtcqBSpUIwQHTDN0zPsJFSXwfvv49/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Qant59tnqFD0fO3aO5mYJrdaOJPXx+OPlzJtXqc7hwoVGzp71jOl5iA0bslm5MtlKcLKely7V\ns2XLWnWOfX19vPNOs6rnsrIQTz65SdXz8PAwb799gXDYgWSAnEIvL7ywNSXuUHBrCONvZhDGn0Ag\nmDM8Hg9DQ0Po9XpKSkpSetLW1TVx/HiE0tLVSJJEb+8Znn66gHnzJkfDXJ94PE5PTw+xWIz8/PyU\nvrygGAY9PT2Ew2GysrJUo2kiTqcTv9+PzWZTDYqJDA0NMTo6SlpaGsXFxVOM05GREVwuF0ajkZKS\nkimFsn0+HwMDA+h0OoqLi6eU0gmFQjidTgAcDseUmMhYLEZPTw+JRIL8/Hw1Vm6cRCJBT08P0WiU\nnJwcsrOzp6yB0+kkGAySmZmZ0pt4nIGBAbxeLxaLZUqyBihG7PDwMCaTiZKSkimdLm6kZ1Dcy06n\nE61Wi8PhSOk9DErCRW9vL5IkUVhYOCVWLpFI0N3dTSwWIy8vLyXpZnyON9NzX18fPp+PjIyMaXtQ\n362e/X4//f3919Wz4NYQxt/MIIw/gUBwX/L228cZHV2MzaYE9rtcPSxaNMTmzTOXcSwQCB4sHvQ6\nfwKBQCC4AdnZFgKBZHJEKOQiM3NmMoEFAoFgnPu9vZtAIBA8MqxevQSn8wS9vW4kKUFFBSxatGyu\nhyUQCB4yhNtXIBAI7iPi8TgjIyNoNBqys7OnxJIJBIJHCxHzNzMI408gEAgEAsEDwWwYf8LtKxAI\nBA8Q0WiU3t5eZFmmsLBQLQ8yTiKRoLe3V80CnZwJK8syvb29hMNhsrOzp2TCgtI6zO/3k5GRkdKy\naxyXy6VmgTocjilZoKOjo7jdbgwGA8XFxdNmgU7M9p1czzAcDtPX1wdAYWHhtNm+vb29JBIJCgoK\npmTCKpnSvUQiEXJyclLako0zMds3Ly9vinxwcBCv14vVap02E3Z4eJjh4WHMZjMOh2PKDq3X61Wz\nfYuLi6dk+4ZCIfr6+tBoNDgcjiklUKLRKE6nE0mSKCgoSGm9Bql6zs3NnTar2+l0EgqFyMrKSmk/\nN87d6tnj8eByua6r50AgwMDAgJrRLLJ97x/Ezp9Ab+rCBQAAIABJREFUIBA8IITDYfbsOYbLlYNG\no8dq7eOllzaoL/5EIsG77x6nszMNnc6CTtfLiy+uVF/ssizzwQdnaGpKoNfbkSQnTz89n8rKCvUZ\n5883cOrUiFrnb8uW/Cn13957rxOttghJGmHFChNbt65T5U6nk717G4ESEgkfVVUxnnpqs2ocDQ8P\ns3v3OSIRB5IUpbBwlOef36oaBoFAgD17TjA6mg8odf5eemmzauDFYjHefvsYTqcNjcaEyeTkpZfW\nqsbNeJ2/K1e06HQ2oJfnn1+aUq7l5MnzXLgQHKvz18+uXaUsXrxAlTc2tnDkSB9abQGS5Gb9ehvr\n16fW+du3rxWNxkE87mHJEg07d6bW+duz5wKxWDGSFKKkxM+zzybrOXq9XnbvPoXfXwhIZGW5eOml\nLaqRG4lE2Lv3GEND2YAei6WPl1+equdr18xotVa02t4p9RyPHDlLU1Mcnc6OLPfx5JNVU+r8nTzp\nRqvNQ5IG2bw5j1WrqlV5W1s7Bw92oNFMr2elBV0DklSMJAWorIzw1FObVQNwZGSE3bvPKnX+pCj5\n+SO8+OI2YQDeASLbVyAQCB5hLl++yvCwg9LS1ZSULCceX8z585dVeXd3N52dFsrK1lNcXI3Fsobj\nx5tV+dDQEJcuRSkt3YTDsZS8vM0cPtzM+B/Efr+fM2f6cTi2UFy8lKKiLZw61U04HAYUo+PIkVYK\nChR5cfEmGhr8DA8Pq884cuQSNlsNDsdSSks30N5uoLe3V5WfPduCRlNNcfEySkvX0N+fx9WrHaq8\nqakNn6+CkpKVlJSsIBicR0PDFVXe2dmJ05lNaelaSkqWodev4PTp5Bz7+vq4ckVDaelGHI6l2O0b\nOHy4SZWPjo5SVzdKcfFmiouXUli4haNH29TWZ9FolOPHOyksVObocGymtnYIr9er3uP995vIzlbu\nX1a2iZaWOAMDyfZsJ082YzSuori4mtLSdXR32+jq6lLl9fWtRKMLKSlZQUnJKjyeEpqb21R5a+tV\nhoYKKSlR9JxILKG2tkWV9/T00NmZRmlpDcXF1Vita1P07HK5aGoKU1Ki6Dk3N1XPgUCA06edqp4d\njq2cOtWjtriTJIkPPrhMXp6yRiUlm7l40Z/Swu7IkUtkZKwfm2MNHR3GFD2fO9cCJPU8NFRAW1s7\ngvsDYfwJBALBA0IoFMNoTLo4zeZ0/P6o+jkajaLTpcoDgaQ8Fouh01nUHSqTyUIkIiNJkirXaMzq\n7o1ebwBMxGIxQElGice1GI1KQWKtVotWayEaTT4jGIxhNiddlFqtVb0eIBCIYTYnx2gwpBMOT5RH\nMZmScpMpdQ7hcAydzjpBbiUQSF4fi8XQalPlwWBSHo1Gx7piaMeebyKR0KtjjMViyLIBg0HZodLp\ndGg0aapckiQiESlljjpdesoc/f4oaWnJOej16UQiqWtkMiWvNxrTCYWS10/Wc1paesocJ+s5LS31\n52CqntOIxzVqD+eknvVj49ej0ZhT9ByLaTCZlJ1IjUaDTmdN0XMolDoHrTZ9ip4nyifrWTC3CONP\nIBAIHhDKyvIJha4SCvmJRsOMjFxm/vxkd4q8vDw0mm78/lFisSj9/ZdYuDApz87Oxmh0Mzo6SDwe\no7e3maoqu2rsZWRkkJkZweXqJh6PMTjYSU5OQo03M5lMlJam0dfXSjweY2Skn7S00ZSYuoUL8+jv\nv0QsFsXnG0ardaZ0l5g/Px+3u4VYLEIw6CMabcfhSMbcVVbm4/NdIRIJEomE8HpbqaxMzqGwMA9J\nukYg4CEWi+BytbBgQVKek5ODwdCP1+smFovS13eJBQuS97fb7VitXtxuJ/F4jP7+NoqKDGqHDYvF\nQkGBlsHBduLxGMPDvWRkBNXYSa1Wy/z5OTidl4jHY3g8QxgMgymxkwsX5jMw0EwsFiUQ8CDLXeTn\nJ8dQUZHH6GgrkUiIcDhAINBGWVlyDqWl+UQi7aqe3e4WqqqS1+fl5aHV9uD3j0yr56ysLEymYUZG\nBojHYzidLVRU2FS3c0ZGBnZ7lKGhLuLxGEND18jOjqt6NhqNlJVZ6eu7PEHPIyl6XrBgsp57p+h5\nePgy0Wh4Wj0L5hYR8ycQCAQPEG1t7Zw+fZV4XGLlyhJWrlyaEojvdDo5erSZUCjGokUF1NSsSAnE\nd7vdHDnSgMcTprw8i82bV6UkG/h8Po4cqWNw0E9hYQbbtq1KSagIhUIcP15Hd/co2dkWtm9fkWIU\nxONxTp+u58qVIaxWI9u2LU1pESdJEnV1TTQ0ODEadWzcmBpzCNDc3EptbSeyDGvWlFFdvThFfu1a\nFydPthKJJKiuLmLNmmUpCReDg4McOdKI3x9l3rxcNm5cmZJUMjo6ygcf1DM8HKS4OJNt21anJJUE\ng0GOHaujt9dDbq6V7dtXpiRURKNRTpyoo7NzmIwME9u3L0tJGkkkEpw710BLywBms54tWxZRUpLs\nLSzLMo2NLdTVdaPRQE1NFQsXzk+Z49WrHZw61XZLel64MJ+amhUpSSUT9VxWlsWWLben53A4rOrZ\nbjezffuKFAN3op4tFgPbt1dfV88Gg5ZNmxZM0bPg1hClXmYGYfwJBAKBQCB4IBAJHwKBQCAQCASC\nu0LU+RMIBIJbJBqNcvZsI/39XvLyrKxfvyzFXZhIJLhwoYmurmFsNjM1NUtT6uzJskxT02VaWwdI\nS9Ozfv2ilDgpUNx9DQ3d6HQa1qypSilRAoq7r7b2KomEzLJlJcyfX5Uid7vdnD17mWAwxsKFBVRX\nL0pxF/p8Ps6caRpzB2azatXSFHdhKBTi3Lkm1R24bt2yFHdhLBbj/PkmenpGycpKo6amOsVdKEkS\nFy82097uwmo1UFOzZEqdvcuX27h0qReDQce6dfNT3IUAXV3d1NV1ArBiRRkVFeUp8sHBQc6eVdy+\nS5YUsXjxgpQ5jo6OcvZsMz5fhMrKXFasWJLi+g4EApw924TbHcThyGTt2uqUEiTRaJRz5xrp6/OS\nm2ulpmaqnuvrL9HZ6cZmM7N+/ZIUt7Asy1y61Mrly/2kpelZt27hlFqC7e0dXLx4fT339fVRW6u4\n96uri1mwYF6KfKKe58/PZ9myRSmub5/Px9mzlxgdDU2r53A4zNmzjdfVczwe5/z5Jrq7R66r54aG\nFq5eVdy+NTWLp9SMbG1t49IlJ3q9dlo9C+aOudj5+zLQAYSAc8DWG5xbAUjTfD016bxPA/VAAOgD\nfgxMrVgpEAgEd4gsy7z33mkaG83E42toabHz7run1AxKgJMnL3D6dJxYbA1dXQ727DmllkkBuHix\nmcOHR4hGVzE0NI/du8+nlBDp7LzGvn3XCIWW4/UuYc+e5pQSIkr9uEt4PIsJhZazf393SpkUn8/H\nm2/WMjBQSTS6ig8+GKW+/pIqj0Qi7NlzimvXiojF1nDmTIKTJy+o8kQiwf79p2hpsRGPr6Gx0cLB\ng6eZGCrzwQfnOH9eSzy+hvb2fN5661RKluf5840cPx4gFltNX185u3efJRAIqPLLl9s4eLCPcHgl\nIyML2b27IaVUjNPp5K23ruDzLcXvr+add9rp6elR5SMjI7z5Zh1u9wKi0VUcOjRES0uyFEwwGGTP\nnjP09JQSi63hxIkwZ89eVOXxeJx33jnF1au5xONrqK83cPjw2Sl6vnjRRDy+htbWLN5552SKnk+d\nquPUqRix2Bq6u4vZs+d0ip4bG1t4/333BD1fwOPxqHKlTuA1gsFlY3puob+/P0XPu3c3Mjq6iFBo\nOQcO9E7R8+7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       "text": [
        "<matplotlib.figure.Figure at 0x11467df90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Identities + home/away + player minutes\n",
      "\n",
      "We add in the number of minutes each player played in each game. This adds many variables to the model and thus makes it much easier to over-fit. We can see that the classification is perfect on the training set, which suggests over fitting. We find that we get better performance on the hold-out set with some added regularization."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.seed(0)\n",
      "gammas = np.linspace(0,.2,40)\n",
      "v_t, v_h = reg_path(games,.3,gammas,5,True, True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Partition 0 . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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       "text": [
        ". "
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       "output_type": "stream",
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       "text": [
        ". "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
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      {
       "output_type": "stream",
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       "text": [
        ". "
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       "text": [
        ". "
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       "text": [
        ". "
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       "text": [
        ". "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Partition 1 . "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ". "
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        ". "
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7wrZtMHy4u6MRosny8/OTLUaEaOH8/NyzEYork79/YR/l+wS4tUb5T8DjLuxH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\no1VxcTGPP16M3++/7sAqPz+fRx65DZ/PF7bQo8uFC1fIzp6J0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       "text": [
        "<matplotlib.figure.Figure at 0x1147eb1d0>"
       ]
      }
     ],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}